{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3b - 1st & 3rd Order Aberrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "nbsphinx-toctree"
    ]
   },
   "source": [
    "### June 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial illustrates how various aberration coefficients are computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optiland.samples.objectives import TripletTelescopeObjective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens = TripletTelescopeObjective()\n",
    "lens.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seidel Aberrations:\n",
      "\tS1: -2.707e-03\n",
      "\tS2: -1.412e-03\n",
      "\tS3: -1.479e-03\n",
      "\tS4: -5.568e-04\n",
      "\tS5: 3.606e-05\n"
     ]
    }
   ],
   "source": [
    "print(\"Seidel Aberrations:\")\n",
    "for k, seidel in enumerate(lens.aberrations.seidels()):\n",
    "    print(f\"\\tS{k + 1}: {seidel:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse spherical aberration:\n",
      "\tSurface 1: -5.087e-01\n",
      "\tSurface 2: -2.442e-01\n",
      "\tSurface 3: 2.466e-02\n",
      "\tSurface 4: -2.759e+00\n",
      "\tSurface 5: 3.481e+00\n",
      "\tSurface 6: -5.623e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse spherical aberration:\")\n",
    "for k, value in enumerate(lens.aberrations.TSC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal spherical aberration:\n",
      "\tSurface 1: -2.849e+00\n",
      "\tSurface 2: -1.367e+00\n",
      "\tSurface 3: 1.381e-01\n",
      "\tSurface 4: -1.545e+01\n",
      "\tSurface 5: 1.949e+01\n",
      "\tSurface 6: -3.149e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal spherical aberration:\")\n",
    "for k, value in enumerate(lens.aberrations.SC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order sagittal coma:\n",
      "\tSurface 1: -2.491e-02\n",
      "\tSurface 2: 2.011e-02\n",
      "\tSurface 3: 4.053e-03\n",
      "\tSurface 4: 8.930e-02\n",
      "\tSurface 5: -9.345e-02\n",
      "\tSurface 6: 9.330e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order sagittal coma:\")\n",
    "for k, value in enumerate(lens.aberrations.CC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order tangential coma:\n",
      "\tSurface 1: -7.473e-02\n",
      "\tSurface 2: 6.034e-02\n",
      "\tSurface 3: 1.216e-02\n",
      "\tSurface 4: 2.679e-01\n",
      "\tSurface 5: -2.803e-01\n",
      "\tSurface 6: 2.799e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order tangential coma:\")\n",
    "for k, value in enumerate(lens.aberrations.TCC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse astigmatism:\n",
      "\tSurface 1: -1.220e-03\n",
      "\tSurface 2: -1.657e-03\n",
      "\tSurface 3: 6.659e-04\n",
      "\tSurface 4: -2.890e-03\n",
      "\tSurface 5: 2.509e-03\n",
      "\tSurface 6: -1.548e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse astigmatism:\")\n",
    "for k, value in enumerate(lens.aberrations.TAC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal astigmatism:\n",
      "\tSurface 1: -6.831e-03\n",
      "\tSurface 2: -9.280e-03\n",
      "\tSurface 3: 3.729e-03\n",
      "\tSurface 4: -1.618e-02\n",
      "\tSurface 5: 1.405e-02\n",
      "\tSurface 6: -8.670e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal astigmatism:\")\n",
    "for k, value in enumerate(lens.aberrations.AC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse Petzval sum:\n",
      "\tSurface 1: -1.850e-03\n",
      "\tSurface 2: -9.425e-05\n",
      "\tSurface 3: -1.636e-03\n",
      "\tSurface 4: -5.416e-04\n",
      "\tSurface 5: 1.167e-03\n",
      "\tSurface 6: 1.395e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse Petzval sum:\")\n",
    "for k, value in enumerate(lens.aberrations.TPC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal Petzval sum:\n",
      "\tSurface 1: -1.036e-02\n",
      "\tSurface 2: -5.278e-04\n",
      "\tSurface 3: -9.159e-03\n",
      "\tSurface 4: -3.033e-03\n",
      "\tSurface 5: 6.537e-03\n",
      "\tSurface 6: 7.812e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal Petzval sum:\")\n",
    "for k, value in enumerate(lens.aberrations.PC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order distortion:\n",
      "\tSurface 1: -1.503e-04\n",
      "\tSurface 2: 1.443e-04\n",
      "\tSurface 3: -1.593e-04\n",
      "\tSurface 4: 1.111e-04\n",
      "\tSurface 5: -9.870e-05\n",
      "\tSurface 6: 2.540e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order distortion:\")\n",
    "for k, value in enumerate(lens.aberrations.DC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order transverse axial color:\n",
      "\tSurface 1: -1.893e-01\n",
      "\tSurface 2: -1.120e-01\n",
      "\tSurface 3: -5.758e-02\n",
      "\tSurface 4: -2.546e-01\n",
      "\tSurface 5: 7.011e-01\n",
      "\tSurface 6: -1.541e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order transverse axial color:\")\n",
    "for k, value in enumerate(lens.aberrations.TAchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order longitudinal axial color:\n",
      "\tSurface 1: -1.060e+00\n",
      "\tSurface 2: -6.274e-01\n",
      "\tSurface 3: -3.224e-01\n",
      "\tSurface 4: -1.426e+00\n",
      "\tSurface 5: 3.926e+00\n",
      "\tSurface 6: -8.627e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order longitudinal axial color:\")\n",
    "for k, value in enumerate(lens.aberrations.LchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order lateral color:\n",
      "\tSurface 1: -9.272e-03\n",
      "\tSurface 2: 9.230e-03\n",
      "\tSurface 3: -9.461e-03\n",
      "\tSurface 4: 8.240e-03\n",
      "\tSurface 5: -1.882e-02\n",
      "\tSurface 6: 2.556e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order lateral color:\")\n",
    "for k, value in enumerate(lens.aberrations.TchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  }
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